LibLINEAR (original) (raw)

A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier).
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008). LIBLINEAR - A Library for Large Linear Classification. URL http://www.csie.ntu.edu.tw/\~cjlin/liblinear/.

BibTeX:

@misc{Fan2008, author = {Rong-En Fan and Kai-Wei Chang and Cho-Jui Hsieh and Xiang-Rui Wang and Chih-Jen Lin}, note = {The Weka classifier works with version 1.33 of LIBLINEAR}, title = {LIBLINEAR - A Library for Large Linear Classification}, year = {2008}, URL = {http://www.csie.ntu.edu.tw/\~cjlin/liblinear/} }

Valid options are:

-S Set type of solver (default: 1) 0 = L2-regularized logistic regression 1 = L2-loss support vector machines (dual) 2 = L2-loss support vector machines (primal) 3 = L1-loss support vector machines (dual) 4 = multi-class support vector machines by Crammer and Singer

-C Set the cost parameter C (default: 1)

-Z Turn on normalization of input data (default: off)

-N Turn on nominal to binary conversion.

-M Turn off missing value replacement. WARNING: use only if your data has no missing values.

-P Use probability estimation (default: off) currently for L2-regularized logistic regression only!

-E Set tolerance of termination criterion (default: 0.01)

-W Set the parameters C of class i to weight[i]*C (default: 1)

-B Add Bias term with the given value if >= 0; if < 0, no bias term added (default: 1)

-D If set, classifier is run in debug mode and may output additional info to the console